{
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  "metadata": {
    "colab": {
      "name": "DeadOil_WellTest_EDA.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP1YvkfDOHlYZhgerF875I7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/DeadOil_WellTest_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZauLkt0cGPRe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "foj4Uh9jHh8t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_excel('/content/DeadOilWell.xlsx')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKSLTnYNHqCJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.iloc[:,:2]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBpJXaHvHstR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "7b6ef3cf-e2fd-4776-91f3-711c3017224b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pressure_Elapsed time</th>\n",
              "      <th>Pressure_Pressure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(hr)</td>\n",
              "      <td>(psia)                                        ...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2223.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001</td>\n",
              "      <td>2203.7</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.003</td>\n",
              "      <td>2151.33</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.004</td>\n",
              "      <td>2067.73</td>\n",
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              "  </tbody>\n",
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            "text/plain": [
              "  Pressure_Elapsed time Pressure_Pressure                                                     \n",
              "0                  (hr)  (psia)                                        ...                    \n",
              "1                     0                                            2223.11                    \n",
              "2                 0.001                                             2203.7                    \n",
              "3                 0.003                                            2151.33                    \n",
              "4                 0.004                                            2067.73                    "
            ]
          },
          "metadata": {
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          "execution_count": 6
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "URREeMQjH4_w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1b6616ed-0f7d-46bf-d98e-7c17210db7e7"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28234"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKmc9UrMH6x7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.columns = ['t','Pwf']"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RivVDdeOIWsZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.iloc[1:,:]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MGEKSQSIYC7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df[:-1]"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OixvjZt7JHL1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "f763c8dc-f084-47f2-e7da-6c63a69d3a0a"
      },
      "source": [
        "df['Pwf'] = pd.to_numeric(df['Pwf'])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9KgjHe5JuCm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8a037a65-d862-4126-f1f6-526485b267ca"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>Pwf</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000</td>\n",
              "      <td>2223.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001</td>\n",
              "      <td>2203.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.003</td>\n",
              "      <td>2151.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.004</td>\n",
              "      <td>2067.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.006</td>\n",
              "      <td>1954.69</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       t      Pwf\n",
              "1  0.000  2223.11\n",
              "2  0.001  2203.70\n",
              "3  0.003  2151.33\n",
              "4  0.004  2067.73\n",
              "5  0.006  1954.69"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uh5tOtd9JwNK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "6d3658d2-87cf-4856-8c64-878b51b0530d"
      },
      "source": [
        "Pi = 2223.11\n",
        "df['delP'] = abs(Pi - df['Pwf'])\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
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          "name": "stderr"
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          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>Pwf</th>\n",
              "      <th>delP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000</td>\n",
              "      <td>2223.11</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001</td>\n",
              "      <td>2203.70</td>\n",
              "      <td>19.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.003</td>\n",
              "      <td>2151.33</td>\n",
              "      <td>71.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.004</td>\n",
              "      <td>2067.73</td>\n",
              "      <td>155.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.006</td>\n",
              "      <td>1954.69</td>\n",
              "      <td>268.42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       t      Pwf    delP\n",
              "1  0.000  2223.11    0.00\n",
              "2  0.001  2203.70   19.41\n",
              "3  0.003  2151.33   71.78\n",
              "4  0.004  2067.73  155.38\n",
              "5  0.006  1954.69  268.42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdkWHubnKIvj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "5d1c901d-1e3d-499d-ccbb-80c1dd1a1b43"
      },
      "source": [
        "df['d(delP)'] = np.zeros(len(df))\n",
        "\n",
        "dp = abs(df['delP'].diff())\n",
        "\n",
        "df['d(delP)'] = dp\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>Pwf</th>\n",
              "      <th>delP</th>\n",
              "      <th>d(delP)</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000</td>\n",
              "      <td>2223.11</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001</td>\n",
              "      <td>2203.70</td>\n",
              "      <td>19.41</td>\n",
              "      <td>19.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.003</td>\n",
              "      <td>2151.33</td>\n",
              "      <td>71.78</td>\n",
              "      <td>52.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.004</td>\n",
              "      <td>2067.73</td>\n",
              "      <td>155.38</td>\n",
              "      <td>83.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.006</td>\n",
              "      <td>1954.69</td>\n",
              "      <td>268.42</td>\n",
              "      <td>113.04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       t      Pwf    delP  d(delP)\n",
              "1  0.000  2223.11    0.00      NaN\n",
              "2  0.001  2203.70   19.41    19.41\n",
              "3  0.003  2151.33   71.78    52.37\n",
              "4  0.004  2067.73  155.38    83.60\n",
              "5  0.006  1954.69  268.42   113.04"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXaeCKCSKhvN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "a8a3bf4a-129d-4ede-9857-4cc3162cfc57"
      },
      "source": [
        "df['dt'] = np.zeros(len(df))\n",
        "\n",
        "dt = abs(df['t'].diff())\n",
        "\n",
        "df['dt'] = dt\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"\n"
          ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>Pwf</th>\n",
              "      <th>delP</th>\n",
              "      <th>d(delP)</th>\n",
              "      <th>dt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000</td>\n",
              "      <td>2223.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001</td>\n",
              "      <td>2203.70</td>\n",
              "      <td>19.41</td>\n",
              "      <td>19.41</td>\n",
              "      <td>0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.003</td>\n",
              "      <td>2151.33</td>\n",
              "      <td>71.78</td>\n",
              "      <td>52.37</td>\n",
              "      <td>0.002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.004</td>\n",
              "      <td>2067.73</td>\n",
              "      <td>155.38</td>\n",
              "      <td>83.60</td>\n",
              "      <td>0.001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.006</td>\n",
              "      <td>1954.69</td>\n",
              "      <td>268.42</td>\n",
              "      <td>113.04</td>\n",
              "      <td>0.002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       t      Pwf    delP  d(delP)     dt\n",
              "1  0.000  2223.11    0.00      NaN    NaN\n",
              "2  0.001  2203.70   19.41    19.41  0.001\n",
              "3  0.003  2151.33   71.78    52.37  0.002\n",
              "4  0.004  2067.73  155.38    83.60  0.001\n",
              "5  0.006  1954.69  268.42   113.04  0.002"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOyesgCjK846",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "0ada19b7-2d74-4278-cbd5-4fb932300ae4"
      },
      "source": [
        "df['P-dash'] = df['t']*df['d(delP)']/df['dt']"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vo_nnWzqLHg1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "37c70e77-5fc6-475b-d3dc-aeab97f83ac6"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>Pwf</th>\n",
              "      <th>delP</th>\n",
              "      <th>d(delP)</th>\n",
              "      <th>dt</th>\n",
              "      <th>P-dash</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000</td>\n",
              "      <td>2223.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001</td>\n",
              "      <td>2203.70</td>\n",
              "      <td>19.41</td>\n",
              "      <td>19.41</td>\n",
              "      <td>0.001</td>\n",
              "      <td>19.410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.003</td>\n",
              "      <td>2151.33</td>\n",
              "      <td>71.78</td>\n",
              "      <td>52.37</td>\n",
              "      <td>0.002</td>\n",
              "      <td>78.555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.004</td>\n",
              "      <td>2067.73</td>\n",
              "      <td>155.38</td>\n",
              "      <td>83.60</td>\n",
              "      <td>0.001</td>\n",
              "      <td>334.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.006</td>\n",
              "      <td>1954.69</td>\n",
              "      <td>268.42</td>\n",
              "      <td>113.04</td>\n",
              "      <td>0.002</td>\n",
              "      <td>339.120</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       t      Pwf    delP  d(delP)     dt   P-dash\n",
              "1  0.000  2223.11    0.00      NaN    NaN      NaN\n",
              "2  0.001  2203.70   19.41    19.41  0.001   19.410\n",
              "3  0.003  2151.33   71.78    52.37  0.002   78.555\n",
              "4  0.004  2067.73  155.38    83.60  0.001  334.400\n",
              "5  0.006  1954.69  268.42   113.04  0.002  339.120"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43rwr0YhLJo5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "46927130-2ff1-4b80-9750-346f6fce64fc"
      },
      "source": [
        "df['dP'] = abs(df['Pwf'].diff())\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>Pwf</th>\n",
              "      <th>delP</th>\n",
              "      <th>d(delP)</th>\n",
              "      <th>dt</th>\n",
              "      <th>P-dash</th>\n",
              "      <th>dP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000</td>\n",
              "      <td>2223.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.001</td>\n",
              "      <td>2203.70</td>\n",
              "      <td>19.41</td>\n",
              "      <td>19.41</td>\n",
              "      <td>0.001</td>\n",
              "      <td>19.410</td>\n",
              "      <td>19.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.003</td>\n",
              "      <td>2151.33</td>\n",
              "      <td>71.78</td>\n",
              "      <td>52.37</td>\n",
              "      <td>0.002</td>\n",
              "      <td>78.555</td>\n",
              "      <td>52.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.004</td>\n",
              "      <td>2067.73</td>\n",
              "      <td>155.38</td>\n",
              "      <td>83.60</td>\n",
              "      <td>0.001</td>\n",
              "      <td>334.400</td>\n",
              "      <td>83.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.006</td>\n",
              "      <td>1954.69</td>\n",
              "      <td>268.42</td>\n",
              "      <td>113.04</td>\n",
              "      <td>0.002</td>\n",
              "      <td>339.120</td>\n",
              "      <td>113.04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       t      Pwf    delP  d(delP)     dt   P-dash      dP\n",
              "1  0.000  2223.11    0.00      NaN    NaN      NaN     NaN\n",
              "2  0.001  2203.70   19.41    19.41  0.001   19.410   19.41\n",
              "3  0.003  2151.33   71.78    52.37  0.002   78.555   52.37\n",
              "4  0.004  2067.73  155.38    83.60  0.001  334.400   83.60\n",
              "5  0.006  1954.69  268.42   113.04  0.002  339.120  113.04"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UGUHFMiBLSnB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "6090fa4d-a56c-41a0-a7de-18de6eaded91"
      },
      "source": [
        "df[\"tdp/dt\"] = df['t']*df['dP']/df['dt']\n",
        "\n",
        "plt.loglog(df['t'],df['tdp/dt'])"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f12de1c9320>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5r_TxypL1Lf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "outputId": "a7bd1754-b0c2-42e5-9041-970332c59d40"
      },
      "source": [
        "plt.style.use('default')\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.loglog(df['t'],df['P-dash'])\n",
        "plt.loglog(df['t'],df['delP'])\n",
        "\n",
        "plt.grid()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHNOeCasMCPv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "semilog = df[(df['t']>0.02) & (df['t']<0.9) ]"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzwjgVpdNIvv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2 = semilog"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jb8zXWaVNKJ3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "1f52b032-4784-4879-e3fd-259cc501939e"
      },
      "source": [
        "plt.scatter(np.log(df2['t']),df2['Pwf'])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f12ddccda20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjbSuNHMNXUP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "outputId": "8de9db49-6118-4b8f-f4f3-e65da045ea18"
      },
      "source": [
        "plt.figure(figsize=(10,4))\n",
        "\n",
        "plt.plot(df['t'],df['Pwf'],marker='.',label='Cartesian Pwf v/s time')\n",
        "\n",
        "plt.axvline(x=12,color='red',linewidth=5,label='Shut In')\n",
        "\n",
        "plt.grid()\n",
        "plt.legend()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f12daf98a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7XErTHosOYhR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U4DXbf5vNvxw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "06096f05-2c8e-45e7-b2d1-06bec3823532"
      },
      "source": [
        "plt.style.use('default')\n",
        "plt.figure(figsize=(8,4))\n",
        "\n",
        "plt.loglog(df3['t'],df3['P-dash'],marker='x',label='Derivative')\n",
        "plt.loglog(df3['t'],df3['delP'],marker='.',label='Pressure Differential')\n",
        "plt.axhline(y = 40,color='red',linewidth=5,label='IARF Reference')\n",
        "\n",
        "plt.xlim(0.0001,0.1)\n",
        "plt.ylim(1,10000)\n",
        "\n",
        "plt.axvspan(0.02,0.1,color='green',alpha=0.5)\n",
        "\n",
        "plt.legend(loc='upper left')\n",
        "plt.grid()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RHfVZa7N6L3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mg-cYy7UOB1k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}